We first compare our sound-guided image manipulation results with existing text-guided manipulation approaches, including TediGAN [1]} and StyleCLIP [2]}. We also compare our approach with the sound-guided image manipulation approach from our prior work [3]}. As can be observed in Fig. REF , image manipulation driven by sounds produces better manipulation quality (compare (b) and (c) vs. (d) and (e)). Furthermore, unlike text-guided methods, the audio-guided approach achieves natural image style transfer and is capable of representing multiple labels. For example, TediGAN emphasizes crying, whereas StyleCLIP focuses on the baby when the text “baby crying” is given. On the contrary, our proposed method can simultaneously handle “baby” and “crying”. Moreover, our proposed method produces more visually plausible manipulation results than does our prior work [3]}, which often produced color shifts (e.g., skin tones).
